Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add uttambharadwaj/kb-graph --skill debriefgit clone --depth 1 https://github.com/uttambharadwaj/kb-graphWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/uttambharadwaj/kb-graph/debrief)<a href="https://agentmods.dev/skills/uttambharadwaj/kb-graph/debrief"><img src="https://agentmods.dev/badge/skills/uttambharadwaj/kb-graph/debrief.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.02229 |
| Opus 5 | $0.00038 | $0.01115 |
| Sonnet 5 | $0.00015 | $0.00446 |
| Haiku 4.5 | $0.00008 | $0.00223 |
Grade A, and why
debrief scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debrief
Extract experiential knowledge from the current conversation and save it to the knowledge base via the kb_* MCP tools.
Note: the nightly harvest job auto-extracts lessons from session transcripts, so a session that never runs this skill still leaves something behind. It extracts facts only if the host set KB_HARVEST_FACTS=1, which is off by default — assume it is off, and that a fact you skip here is simply not recorded. Running /debrief is also the higher-quality pass for lessons: richer context, better titles, immediate availability. Your in-context judgment beats the transcript-level pass — don't skip candidates just because "harvest will get it."
Division of labor: user preferences and standing corrections belong in your agent's own memory system. Project state, technical knowledge, decisions, gotchas, and facts belong in the KB. During debrief, write only to the KB.
Step 1: Scan the conversation
Review the full conversation and extract candidates.
Strong signals (almost always extract):
- Problem → root cause → fix chains →
lesson - "It turns out..." / "The actual reason was..." moments →
lesson - Explicit decisions with reasoning ("we chose X because...") →
decision - Commands/workflows that were non-obvious →
workflow - User saying "remember this" / "save this" → pick the type that fits
- Debugging that revealed how a system works →
idea(mental model)
Skip:
- Exploratory reads that didn't yield insight
- Failed attempts that didn't teach anything reusable
- Things already documented elsewhere (reference them instead)
- Session-specific decisions that won't matter next time
Context updates (type: session — the nightly job folds these into the per-workstream state note, so write them freely):
- Project status materially changed (PR merged, blocker hit, phase completed)
- New workstream started; workstream completed or paused
Temporal facts (via kb_extract / kb_fact_add): subject-predicate-object triples with dates — PR #123 shipped_via commit abc123, TICKET-42 blocked_by TICKET-43, service-x deployed_to prod.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 117 lines · 76 tokens per session scan A 96eb8c1d37dc
debrief is a skill published in the GitHub repository uttambharadwaj/kb-graph (3 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 2,229 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.